Our MLOps methodology delivers scalable AI models quickly and effectively.
Machine Learning (ML) is a form of AI that lets a system continuously learn from data through virtuous algorithms rather than explicit programming. It offers potential value for companies that use data to better understand the subtle changes in their customers’ behaviours, preferences and levels of satisfaction.
But despite these capabilities, machine learning also comes with challenges and risks. Firstly, complex ML models need to be regularly refreshed, which can incur high production deployment costs. Secondly, if data quality is not closely monitored, the AI can quickly suffer from performance drift and bias.
To solve these challenges, we close the gap between Proofs of Concepts (POC) and Production by applying our Machine Learning Operations (MLOps) methodology to all of our Data and AI projects.
Our methodology is inspired by the DevOps approach used by the most innovative software companies, combining software development (Dev) and IT operations (Ops).
It aims to shorten the systems development life cycle and provide continuous delivery with high software quality.
Our MLOps approach helps companies seamlessly industrialise and scale their AI products.
The traditional approach of using Machine Learning’s capabilities has several drawbacks:
Data Scientists hardly foresee production constraints. They work in silos without interaction with software or data engineers. Their one-shot analyses in Python notebooks need to be reworked by downstream engineers to fit industrialisation requirements. This induces slowness and reduces time to market.
A lack of agility, which leads to high operational risk. In case the produced algorithms reveal themselves biased, unstable or prone to customer dissatisfaction, companies will not be able to respond in an acceptable time frame.
We think “product first” to help companies progress their AI assets smoothly to production while anticipating industrialisation constraints and risks. Our MLOps model is based on a solid ecosystem, and we apply the same processes for every AI project we deliver, from POC to product deployment.
SUCCESSFUL MLOPS APPROACH
To avoid the common pitfalls faced by many organisations looking to accelerate their data transformation.
A solid monitoring stack.
We test all data, features and models before every new release to prevent quality or performance drift.
Our data, models and learning experiments are all versioned and logged to ensure fast rollback in case of production incidents.
A resilient machine learning infrastructure.
We embed all Machine Learning assets (code, data, models) in a Continuous Integration and Continuous Delivery pipeline (CICD) to secure fast and seamless rollouts to production.
A strong collaboration culture.
We ensure all stakeholders work on the same canvas and apply software engineering best practices to Data Science projects (versioning, deployment environments, testing).
Read our Data Science blog post explaining how we apply MLOPS for our clients.